Domain Generalization Techniques and Datasets.
A number of domain generalization algorithms have been proposed in the ML community in the past few years. Most of them fall into one of three categories [1]}:
1) Data manipulation, which augments or generates data to help the model training (e.g., [2]}, [3]});
2) Representation learning, which focuses on learning generalized representations across domains (e.g., [4]}, [5]}, [6]});
3) Learning strategy, which aims to utilize the training procedure to enhance model generalizability (e.g., [7]}, [8]}, [9]}).
Researchers have released multiple datasets such as PACS [10]}, VLCS [11]} and Office-Home [12]}, and developed cross-dataset benchmark platforms such as DomainBed [13]}, DeepDG [1]}, and WILDS [15]} to facilitate related studies.
However, most existing domain generalization research focuses on the tasks of CV and NLP.
